利用治疗前PSMA-PET/CT扫描的原发肿瘤和淋巴结放射组学预测局限性前列腺癌患者的无转移生存期

IF 5.3 1区 医学 Q1 ONCOLOGY
Apurva Singh , William Silva Mendes , Sang-Bo Oh , Ozan Cem Guler , Aysenur Elmali , Birhan Demirhan , Amit Sawant , Phuoc Tran , Cem Onal , Lei Ren
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引用次数: 0

摘要

目的:通过术前PSMA PET/CT扫描提取原发肿瘤和淋巴结体积的临床因素和放射组学,预测前列腺癌(PCa)患者接受雄激素剥夺治疗(ADT)和外放疗的无转移生存期(MFS)。材料/方法:我们的队列包括134例PCa患者(28例淋巴结受累)。对CT和PET扫描的原发肿瘤体积(GTVp)和淋巴结(GTVn)进行分割。原发肿瘤外有5 mm的扩张环区。对肿瘤和环提取的放射组学特征进行Z-score归一化处理;采用主成分分析(PCA)进行降维。对于只有原发肿瘤的患者,我们从GTVp中提取了3个主成分(PCs),并从CT和PET扫描中提取了1个环PC作为代表性放射组学成分。对于有淋巴结的患者,我们计算原发肿瘤和淋巴结放射组学的加权平均值(按体积计算),计算前3个PC,并将其与环上第1个PC合并。放射组学pc和临床变量(年龄、Gleason评分、初始前列腺特异性抗原值(i PSA)、psa_复发)构成预测因子。由于MFS数据不平衡(转移-24,无转移-110),我们进行了70:30训练测试分割,并对训练数据进行了不平衡校正。采用单因素cox -回归选择预测因子(logistic回归p )结果:时间-事件分析(MFS)结果:cox -回归c-得分:模型1:训练- 0.77 [0.72,0.78];Test - 0.69 [0.64, 0.70];模型2:train- 0.72 [0.66, 0.73];Test - 0.63 [0.58, 0.64];模型3:train- 0.62 [0.57, 0.63];测试- 0.54[0.51,0.56]。5 年MFS分类分析结果为[敏感性、特异性、AUC]:模型1:train-[83.6 %,91.3 %,0.88];测试-[76.3 %,82.5 %,0.81];模型2:train-[77.4% %,85.1% %,0.84];测试-[71.5 %,78.2 %,0.76];模型3:train-[69.3 %,78.2 %,0.76];测试-[64.7 %,72.6 %,0.68]。采用模型1分型的两组患者的实际生存曲线差异有统计学意义,说明该分型的有效性。淋巴结与原发肿瘤放射组学的结合在MFS预测中提供了最佳的预后效果。结论:这是第一个探讨PSMA-PET治疗前预后价值的研究之一,这是前列腺癌患者治疗的一个相对较新的进展。结果表明,在治疗前使用PSMA-PET/CT图像中的成像生物标志物进行预后预测的潜力,为临床医生定制治疗模式提供了有价值的信息,以改善原发性前列腺癌患者的预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of metastasis-free survival in patients with localized prostate adenocarcinoma using primary tumor and lymph node radiomics from pre-treatment PSMA-PET/CT scans

Purpose

To predict metastasis-free survival (MFS) for patients with prostate adenocarcinoma (PCa) treated with androgen deprivation therapy (ADT) and external radiotherapy using clinical factors and radiomics extracted from primary tumor and node volumes in pre-treatment PSMA PET/CT scans.

Materials/Methods

Our cohort includes 134 PCa patients (nodal involvement in 28 patients). Gross tumor volumes of primary tumor (GTVp) and nodes (GTVn) on CT and PET scans were segmented. A 5 mm expansion ring area outside primary tumor was defined. Z-score normalization was applied to radiomics features extracted from tumor and ring; dimension reduction was performed using Principal Components Analysis (PCA). For patients with only primary tumor, we took 3 principal components (PCs) from GTVp and one ring PC as representative radiomics components from CT and PET scans. For patients with nodes, we calculated weighted average (by volume) of radiomics from primary tumor and nodes, computed first 3 PCs and combined it with 1st PC from the ring. Radiomics PCs and clinical variables (age, Gleason score, initial prostate specific antigen value (i PSA), PSA_relapse) formed the predictors. Due to MFS data imbalance (metastasis-24, no metastasis-110), we performed 70:30 train-test split and applied imbalance correction to training data. Univariate Cox-regression was used to select top predictors (logistic regression p < 0.05). Multivariate Cox-regression was performed on imbalance-corrected training data and fit on testing data (using predictors selected from training). Model 2 was built using clinical variables and radiomic PCs from primary tumors (GTVp, ring). Model 3 was built using clinical variables only. Binary classification analysis for prediction of five-year MFS was also performed.

Results

Results of time-to-event analysis (MFS) were: Cox-regression c-scores: model1: train- 0.77 [0.72, 0.78]; test- 0.69 [0.64, 0.70]; model2: train- 0.72 [0.66, 0.73]; test- 0.63 [0.58, 0.64]; model3: train- 0.62 [0.57, 0.63]; test- 0.54 [0.51, 0.56]. The results of 5 year MFS classification analysis were [sensitivity, specificity, AUC]: model 1: train- [83.6 %, 91.3 %, 0.88]; test- [76.3 %, 82.5 %, 0.81]; model 2: train- [77.4 %, 85.1 %, 0.84]; test- [71.5 %, 78.2 %, 0.76]; model 3: train- [69.3 %, 78.2 %, 0.76]; test- [64.7 %, 72.6 %, 0.68]. The two cohorts of patients classified by model 1 showed statistically significant differences in their actual survival curves, demonstrating the efficacy of the classification. Integration of node with primary tumor-radiomics provides the best prognostic performance in MFS prediction.

Conclusion

This is one of the first studies to explore the prognostic value of pre-treatment PSMA-PET, a relatively recent advancement in the care of prostate adenocarcinoma patients. Results demonstrated the potential of using imaging biomarkers from PSMA-PET/CT images for prognosis prediction before the treatment, which provides clinicians valuable information for customizing the treatment paradigm to improve the outcomes for primary prostate cancer patients.
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来源期刊
Radiotherapy and Oncology
Radiotherapy and Oncology 医学-核医学
CiteScore
10.30
自引率
10.50%
发文量
2445
审稿时长
45 days
期刊介绍: Radiotherapy and Oncology publishes papers describing original research as well as review articles. It covers areas of interest relating to radiation oncology. This includes: clinical radiotherapy, combined modality treatment, translational studies, epidemiological outcomes, imaging, dosimetry, and radiation therapy planning, experimental work in radiobiology, chemobiology, hyperthermia and tumour biology, as well as data science in radiation oncology and physics aspects relevant to oncology.Papers on more general aspects of interest to the radiation oncologist including chemotherapy, surgery and immunology are also published.
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